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Cited by 13Context-Aware Activity Recognition SystemsIoT-based Smart Home SystemsTime Series Analysis and Forecasting

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Predicting Sensor Events, Activities, and Time of Occurrence Using Binary Sensor Data From Homes With Older Adults

Open access
Casagrande, Flavia D., Torresen, Jim, Zouganeli, Evi·2019·IEEE Access
DOI 10.1109/access.2019.2933994
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Abstract

We present a comprehensive study of state-of-the-art algorithms for the prediction of sensor events and activities of daily living in smart homes. Data have been collected from eight smart homes with real users and 13-17 binary sensors each - including motion, magnetic, and power sensors. We apply two probabilistic methods, namely Sequence Prediction via Enhanced Episode Discovery and Active LeZi, as well as Long Short-Term Memory Recurrent Neural Network, in order to predict the next sensor event in a sequence. We compare these with respect to the required number of preceding sensor events to predict the next, the necessary amount of data to achieve good accuracy and convergence, as well as varying the number of sensors in the dataset. The best-performing method is further improved by including information on the time of occurrence to predict the next sensor event only, and in addition to predict both the next sensor event and the mean time of occurrence in the same model. Subsequently, we apply transfer learning across apartments to investigate its applicability, advantages, and limitations for this setup. Our best implementation achieved an accuracy of 77-87% for predicting the next sensor event, and an accuracy of 73-83% when predicting both the next sensor event and the mean time elapsed to the next sensor event. Finally, we investigate the performance of predicting daily living activities derived from the sensor events. We can predict activities with an accuracy of 61-90%, depending on the apartment.

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Metadata

Authors
Casagrande, F. D., Torresen, J., & Zouganeli, E.
Affiliations
OsloMet – Oslo Metropolitan University (NO)
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2019
Volume
7
Pages
111012-111029
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2019.2933994
OpenAlex
W2967386683
Cited by
13
References
4
Citations by year
2026: 1 · 2025: 1 · 2024: 1 · 2022: 5 · 2021: 4 · 2020: 1
FWCI
1.12
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Access (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • NORA - Norwegian Open Research Archives (submittedVersion, other-oa)
  • NORA - Norwegian Open Research Archives (submittedVersion, cc-by)
Funders
Norges Forskningsråd (247620/O70)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › IoT-based Smart Home Systems
  • Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
Keywords
home automation, older adults, activity recognition, activities of daily living, binary sensor data, long short-term memory, recurrent neural networks, transfer learning, motion sensors, magnetic sensors, power sensors

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CoChat. (2026). Citation check: Predicting Sensor Events, Activities, and Time of Occurrence Using Binary Sensor Data From Homes With Older Adults. https://app.cochat.ai/papers/10.1109/access.2019.2933994
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Casagrande, F. D., Torresen, J., & Zouganeli, E. (2019). Predicting Sensor Events, Activities, and Time of Occurrence Using Binary Sensor Data From Homes With Older Adults. IEEE Access. https://doi.org/10.1109/access.2019.2933994